When building scikit-learn pipelines I often feel I have to do a lot of my data preparation work outside the pipeline. Moreover, many scikit-learn transformers could be more beginer friendly if they returned pandas DataFrames instead of numpy arrays.
With that in mind, this library includes a few additional transformers that are mostly thin wrappers around scikit-learn.
For example:
from more_transformers.preprocessing import StandardScaler
behaves identically to sklearn.preprocessing.StandardScaler but returns a pandas DataFrame with the same column names and index values as the original.
As another example
from more_transformers.decomposition import PCA
is the same as from sklearn.decomposition import PCA but retains the index and uses column names pca_0, pca_1,...,pca_n.
I've also added my own few helpers, mostly under from more_transformers.common. For example
from more_transformers.preprocessing import GetDummies
is a transformer version of pd.get_dummies. One advantage is that if the test data is transformed to have the same columns as pd.get_dummies on the training data.
Also note
from more_transformers.common import ColumnSelector
allows for very flexible selection of columns in your pipeline. For example
ColumnSelector() # Selects all columns
ColumnSelector(['Age','Weight','Height']) # Selects these columns
ColumnSelector('number') # Selects all integer or float columns
ColumnSelector(lambda x: str(x).starts_with('x_')) # Selects columns starting with 'x_'